
DHA Service Access and Coverage
1 Overview
Birth and death registration may be incomplete due to the inaccessibility of Home Affairs offices where registration occurs. A potential barrier to registration may be the distance needed to travel. We aim to determine the proportion of the population within reasonable distances of Home Affairs offices.
2 Methodology
2.1 Data Sources
- Shape files: District boundaries from Stats SA, standardised using the
{NMCleaner}package - Population estimates: Stats SA mid-year population estimates (2025), at district level
- Office locations: DHA office coordinates from existing dataset (excludes hospital satellite offices)
2.2 Analytical Approach
We explored three approaches to estimate population coverage:
Office-to-office distance: Measured median half-distance between nearest office pairs. This underestimated coverage in rural areas.
Uniform grid distribution: Created 1km grid and distributed population uniformly within districts. Ignored population clustering.
Inverse power model (selected): Assumed population clusters around offices with distance decay.
2.3 Population Density Models
Inverse Power Model: \[ w_i = \frac{1}{(d_i + 1)^{\alpha}} \]
where \(d_i\) is the distance (km) from grid point \(i\) to the nearest office, and \(\alpha = 1.5\) is the decay parameter.
Population Allocation:
Weights are normalised within each district: \[ \hat{w}_i = \frac{w_i}{\sum_{j \in d} w_j} \]
The population at each grid point is: \[ P_i = P_d \cdot \hat{w}_i \]
where \(P_d\) is the total population of district \(d\).
2.4 Limitations
- Population is modelled, not observed at sub-district level
- Distances are straight-line, not road-network or travel-time
- Office capacity and service quality are not accounted for
- Satellite offices (e.g., in hospitals) are not included
3 National Overview Maps
3.1 Population Distribution by District
3.2 Number of DHA Offices by District

3.3 Modelled Population Distribution

4 Coverage Summary
District | Total Population | Population within 10 km | % Population within 10 km | Population within 20 km | % Population within 20 km | Median Distance to Nearest DHA Office (km) |
|---|---|---|---|---|---|---|
Alfred Nzo | 935,303 | 455,654 | 49 | 743,056 | 79 | 10 |
Amajuba | 610,841 | 248,305 | 41 | 412,243 | 67 | 7 |
Amathole | 792,612 | 384,504 | 49 | 624,102 | 79 | 14 |
Bojanala Platinum | 1,985,081 | 973,231 | 49 | 1,498,577 | 75 | 7 |
Buffalo City | 0 | 0 | 0 | 7 | ||
Cape Winelands | 1,014,432 | 393,345 | 39 | 660,490 | 65 | 22 |
Capricorn | 1,412,657 | 704,095 | 50 | 1,090,472 | 77 | 7 |
Central Karoo | 77,157 | 19,338 | 25 | 33,406 | 43 | 61 |
Chris Hani | 717,289 | 284,460 | 40 | 477,557 | 67 | 19 |
City of Cape Town | 5,030,497 | 4,099,372 | 81 | 4,680,809 | 93 | 6 |
City of Johannesburg | 5,900,321 | 5,628,983 | 95 | 5,892,801 | 100 | 5 |
City of Tshwane | 4,038,061 | 2,807,261 | 70 | 3,797,729 | 94 | 6 |
Dr Kenneth Kaunda | 807,057 | 306,766 | 38 | 534,494 | 66 | 26 |
Dr Ruth Segomotsi Mompati | 474,901 | 126,707 | 27 | 226,657 | 48 | 37 |
Ehlanzeni | 1,928,692 | 949,462 | 49 | 1,514,775 | 79 | 15 |
Ekurhuleni | 4,059,057 | 3,606,629 | 89 | 4,026,309 | 99 | 4 |
Fezile Dabi | 536,755 | 210,022 | 39 | 354,982 | 66 | 26 |
Frances Baard | 438,829 | 252,122 | 57 | 344,875 | 79 | 7 |
Garden Route | 673,192 | 231,969 | 34 | 359,567 | 53 | 26 |
Gert Sibande | 1,367,513 | 499,243 | 37 | 865,389 | 63 | 24 |
Harry Gwala | 507,708 | 244,652 | 48 | 393,555 | 78 | 15 |
Joe Gqabi | 354,931 | 120,698 | 34 | 201,178 | 57 | 26 |
John Taolo Gaetsewe | 296,434 | 60,904 | 21 | 105,422 | 36 | 59 |
King Cetshwayo | 992,551 | 584,675 | 59 | 878,508 | 89 | 11 |
Lejweleputswa | 698,356 | 237,433 | 34 | 415,209 | 59 | 22 |
Mangaung | 857,973 | 368,652 | 43 | 569,402 | 66 | 7 |
Mopani | 1,266,834 | 637,249 | 50 | 965,505 | 76 | 11 |
Namakwa | 129,515 | 18,623 | 14 | 32,228 | 25 | 102 |
Nelson Mandela Bay | 1,263,632 | 913,990 | 72 | 1,177,353 | 93 | 6 |
Ngaka Modiri Molema | 916,907 | 356,377 | 39 | 585,529 | 64 | 11 |
Nkangala | 1,779,928 | 915,035 | 51 | 1,379,990 | 78 | 15 |
O.R. Tambo | 1,623,984 | 901,159 | 55 | 1,383,467 | 85 | 11 |
Overberg | 329,835 | 129,159 | 39 | 214,445 | 65 | 30 |
Pixley ka Seme | 219,155 | 44,535 | 20 | 78,796 | 36 | 61 |
Sarah Baartman | 527,418 | 137,390 | 26 | 236,924 | 45 | 36 |
Sedibeng | 1,061,185 | 698,345 | 66 | 956,982 | 90 | 5 |
Sekhukhune | 1,333,432 | 623,524 | 47 | 1,030,150 | 77 | 14 |
Thabo Mofutsanyana | 810,097 | 281,906 | 35 | 474,807 | 59 | 21 |
Ugu | 831,709 | 410,976 | 49 | 648,175 | 78 | 16 |
Umzinyathi | 607,975 | 279,573 | 46 | 481,574 | 79 | 20 |
Vhembe | 1,527,097 | 814,784 | 53 | 1,210,958 | 79 | 11 |
Waterberg | 826,172 | 270,753 | 33 | 469,023 | 57 | 20 |
West Coast | 502,576 | 158,658 | 32 | 266,241 | 53 | 54 |
West Rand | 1,046,310 | 595,362 | 57 | 881,713 | 84 | 7 |
Xhariep | 136,652 | 32,346 | 24 | 58,520 | 43 | 33 |
ZF Mgcawu | 295,250 | 65,365 | 22 | 109,046 | 37 | 50 |
Zululand | 901,275 | 416,978 | 46 | 696,859 | 77 | 14 |
eThekwini | 4,374,202 | 3,566,221 | 82 | 4,314,339 | 99 | 4 |
iLembe | 742,038 | 487,512 | 66 | 715,374 | 96 | 14 |
uMgungundlovu | 1,220,477 | 558,333 | 46 | 974,950 | 80 | 18 |
uMkhanyakude | 711,366 | 369,938 | 52 | 571,470 | 80 | 20 |
uThukela | 732,104 | 233,311 | 32 | 401,014 | 55 | 29 |
Total | 62,225,326 | 37,715,880 | 61 | 51,016,996 | 82 | 14 |
5 Interactive Map
Explore DHA office locations with coverage buffers (10km, 20km, 50km).
6 Key Findings
- 61% of the population lives within 10km of a DHA office
- 82% of the population lives within 20km of a DHA office
- Rural districts show significantly lower coverage
7 Recommendations
- Request enumeration area data: Census data at smaller geographic units would improve population distribution estimates
- Include satellite offices: Hospital-based registration points should be mapped
- Road network analysis: Travel time may be more relevant than straight-line distance